System and method for task-less mapping of brain activity

ABSTRACT

A computing device for use in a system for mapping brain activity of a subject includes a processor. The processor is programmed to select a plurality of measurements of brain activity that is representative of at least one parameter of a brain of the subject during a resting state. Moreover, the processor is programmed to compare at least one data point from each of the measurements with a corresponding data point from a previously acquired data set from at least one other subject. The processor is also programmed to produce at least one map for each of the measurements based on the comparison of the resting state data point and the corresponding previously acquired data point. The processor may also be programmed to categorize the brain activity in a plurality of networks in the brain based on the map.

CROSS REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. application Ser. No. 15/237,202, filed Aug. 15, 2016, and incorporated herein in its entirety. U.S. application Ser. No. 15/237,202 is a continuation of U.S. application Ser. No. 13/673,816, filed Nov. 9, 2012, patented as U.S. Pat. No. 9,480,402 and incorporated herein in its entirety. U.S. application Ser. No. 13/673,816 claims priority to U.S. Provisional Application Ser. No. 61/558,751, filed Nov. 11, 2011, and also incorporated herein in its entirety.

BACKGROUND

The field of the invention relates generally to brain mapping systems and, more particularly, to systems and methods for task-less mapping of brain activity using resting state data collected from a brain of a subject.

Brain mapping includes a set of neuroscience techniques that are predicated on the mapping of biological quantities or properties onto spatial representations of a subject's brain resulting in at least one map. At least some known neuroimaging systems or techniques are used frequently in clinical and research settings for brain mapping such that brain function can be monitored. For example, functional magnetic resonance imaging (fMRI) may be used to enable researchers and clinicians to see visual images of the brain, wherein the images may be used to identify brain activity within a plurality of networks of the brain. One approach includes a task based technique wherein the fMRI may be used to detect correlations between brain activation and various tasks that a subject performs during a scan. Such task-based techniques can be useful in clinical applications. For example, the images obtained through fMRI may enable a surgeon to identify portions of the brain that are responsible for various functions and the surgeon may attempt to avoid contact with such portions while performing surgery on the brain.

However, task-based neuroimaging may not be suitable for all segments of a clinical population. For example, a toddler or a nervous patient may be unable to comprehend and/or perform various tasks. It was recently discovered, via fMRI, that even during the absence of overt tasks, fluctuations in brain activity are correlated across functionally-related cortical regions. Thus, the spatial and temporal evaluations of spontaneous neuronal activity has allowed mapping of these resting-state networks (RSNs) with a task-less technique. For this technique, at least one voxel within the image obtained by the fMRI is selected and a correlation analysis is performed to identify other voxels that correspond with the selected voxel. However, it may be challenging to identify which voxel to select. For example, an individual would require a great deal of expertise and resources to select a relevant voxel. In fact, selecting a voxel and/or processing information to select a voxel can be time consuming.

Accordingly, it is desirable to provide a system and method that can readily identify the voxels to select and, at substantially the same time, provide suitable results for accurate brain mapping.

BRIEF DESCRIPTION

In one aspect, a computing device for use in a system for mapping brain activity of a subject generally comprises a processor. The processor is programmed to select a plurality of measurements of brain activity that is representative of at least one parameter of a brain of the subject during a resting state. Moreover, the processor is programmed to compare at least one data point from each of the measurements with a corresponding data point from a previously acquired data set from at least one other subject. The processor is also programmed to produce at least one map for each of the measurements based on the comparison of the resting state data point and the corresponding previously acquired data point. The processor may also be programmed to categorize the brain activity in a plurality of networks in the brain based on the map.

In another aspect, a system for mapping brain activity of a subject generally comprises a sensing system and a computing device that is coupled to the sensing system. The sensing system is configured to detect a plurality of measurements of brain activity that is representative of at least one parameter of a brain of the subject during a resting state. The computing device includes a communication interface that is configured to receive at least one signal representative of the measurements, and a processor that is coupled to the communication interface. The processor is programmed to select the measurements of brain activity. Moreover, the processor is programmed to compare at least one data point from each of the measurements with a corresponding data point from a previously acquired data set from at least one other subject. The processor is also programmed to produce at least one map for each of the measurements based on the comparison of the resting state data point and the corresponding previously acquired data point. The processor may also be programmed to categorize the brain activity in a plurality of networks in the brain based on the map.

In yet another aspect, a method for mapping brain activity of a patient generally comprises selecting, via a processor, a plurality of measurements of brain activity that is representative of at least one parameter of a brain of the subject during a resting state. At least one data point from each of the plurality of measurements is compared, via the processor, with a corresponding data point from a previously acquired data set from at least one other subject. At least one map is produced, via the processor, for each of the measurements based on the comparison of the resting state data point and the corresponding previously acquired data point. The brain activity is categorized, via the processor, in a plurality of networks in the brain based on the map.

BRIEF DESCRIPTION OF THE DRAWINGS

The patent or application file contains at least one drawing executed in color. Copies of this patent or patent application publication with color drawing(s) will be provided by the Office upon request and payment of the necessary fee.

FIG. 1 is a block diagram of an exemplary system for task-less mapping of brain activity;

FIG. 2 is a block diagram of an exemplary computing device of the system shown in FIG. 1;

FIG. 3 is flow diagram of an exemplary method for task-less mapping of brain activity using the system shown in FIG. 1;

FIG. 4 is an image of seed ROIs for generation of correlation map data;

FIG. 5 is a schematic of an exemplary standard multi-layer perceptron architecture and transfer function of the perceptron;

FIG. 6 is a graph of a learning rate;

FIG. 7 is a schematic of a voxel-wise classification;

FIGS. 8A-8D are graphs depicting correlation maps;

FIGS. 9A-9F are graphs depicting performance levels;

FIG. 10 is a graph depicting search space for perceptron architecture;

FIGS. 11A-11F are schematics of topographies in individual subjects;

FIGS. 12A-12C are schematics of classification results;

FIG. 13 is a scan of group averaged results;

FIGS. 14A and 14B are graphs of exemplary evaluations;

FIG. 15 is a scan of voxels; and

FIGS. 16A and 16B are scans of topography estimates.

The patent or application file contains at least one drawing executed in color. Copies of this patent or patent application publication with color drawing(s) will be provided by the U.S. Patent and Trademark Office upon request and payment of the necessary fee.

DETAILED DESCRIPTION OF THE DRAWINGS

The exemplary systems, apparatus, and methods described herein overcome at least some known disadvantages associated with at least some known brain mapping techniques, such as task-based and/or task-less systems. More specifically, the embodiments described herein include a computing device for use in a system for mapping brain activity of a subject that generally comprises a processor. The processor is programmed to select a plurality of measurements of brain activity that is representative of at least one parameter of a brain of the subject during a resting state. Moreover, the processor is programmed to compare at least one data point from each of the measurements with a corresponding data point from a previously acquired data set from at least one other subject. The processor is also programmed to produce at least one map for each of the measurements based on the comparison of the resting state data point and the corresponding previously acquired data point. The processor may also be programmed to categorize the brain activity in a plurality of networks in the brain based on the map. By using previously acquired data points to categorize the brain activity in a plurality of networks in the brain of the subject, task-based techniques can be avoided. Moreover, by having the processor select the plurality of measurements, a user may no longer need to spend a considerable amount of time determining which measurements, such as voxels, to select.

FIG. 1 illustrates an exemplary system 100 for mapping brain activity of a subject (not shown). It should be noted that the term “brain activity” as used herein includes the various activities within a brain of the subject that correspond to various tasks performed by the subject. For example, the brain transmits and receives signals in the form of hormones, nerve impulses, and chemical messengers that enable the subject to move, eat, sleep, and think. In the exemplary embodiment, system 100 is used to identify locations within a plurality of networks within the brain that are responsible for such brain activities.

As seen in FIG. 1, system 100 includes a sensing system 102 that is configured to detect a plurality of measurements of brain activity that is representative of at least one parameter of the brain of the subject during a resting state. In one suitable embodiment, sensing system 102 is a magnetic resonance imaging device (MRI) that is configured to generate at least one spectroscopic signal representative of a plurality of measurements of brain activity that is representative of at least one parameter of the brain of the subject during a resting state. More specifically, sensing system 102 may generate an altered magnetic field within the brain to measure various parameters of the brain. In another suitable embodiment, sensing system 102 may be a specialized MRI, such as a functional magnetic resonance imaging (fMRI) device that is used to measure a variation in blood flow (hemodynamic response) related to neural activity in the brain or spinal cord (not shown) of the subject. In yet another suitable embodiment, sensing system 102 may be an electrocorticography device having at least one electrode (not shown) to measure at least one voltage fluctuation within the brain. It should be noted that the present disclosure is not limited to any one particular type of imaging and electrical technique or device, and one of ordinary skill in the art will appreciate that the current disclosure may be used in connection with any type of technique or device that enables system 100 to function as described herein.

In the exemplary embodiment, system 100 also includes a computing device 104 coupled to sensing system 102 via a data conduit 106. It should be noted that, as used herein, the term “couple” is not limited to a direct mechanical, electrical, and/or communication connection between components, but may also include an indirect mechanical, electrical, and/or communication connection between multiple components. Sensing system 102 may communicate with computing device 104 using a wired network connection (e.g., Ethernet or an optical fiber), a wireless communication means, such as radio frequency (RF), e.g., FM radio and/or digital audio broadcasting, an Institute of Electrical and Electronics Engineers (IEEE®) 802.11 standard (e.g., 802.11(g) or 802.11(n)), the Worldwide Interoperability for Microwave Access (WIMAX®) standard, a short-range wireless communication channel such as BLUETOOTH®, a cellular phone technology (e.g., the Global Standard for Mobile communication (GSM)), a satellite communication link, and/or any other suitable communication means. IEEE is a registered trademark of the Institute of Electrical and Electronics Engineers, Inc., of New York, N.Y. WIMAX is a registered trademark of WiMax Forum, of Beaverton, Oreg. BLUETOOTH is a registered trademark of Bluetooth SIG, Inc. of Kirkland, Wash.

In the exemplary embodiment, computing device 104 is configured to receive at least one signal representative of a plurality of measurements of brain activity from sensing system 102. More specifically, computing device 104 is configured to receive at least one signal representative of an altered magnetic field within the brain of the subject from sensing system 102. Alternatively, computing device 104 may be configured to receive at least one signal representative of at least one voltage fluctuation within the brain from at least one electrode.

System 100 also includes a data management system 108 that is coupled to computing device 104 via a network 109. Data management system 108 may be any device capable of accessing network 109 including, without limitation, a desktop computer, a laptop computer, or other web-based connectable equipment. More specifically, in the exemplary embodiment, data management system 108 includes a database 110 that includes previously acquired data of other subjects. In the exemplary embodiment, database 110 can be fully or partially implemented in a cloud computing environment such that data from the database is received from one or more computers (not shown) within system 100 or remote from system 100. In the exemplary embodiment, the previously acquired data of the other subjects may include, for example, a plurality of measurements of brain activity that is representative of at least one parameter of a brain of each of the subjects during a resting state. Database 110 can also include any additional information of each of the subjects that enables system 100 to function as described herein.

Data management system 108 may communicate with computing device 104 using a wired network connection (e.g., Ethernet or an optical fiber), a wireless communication means, such as, but not limited to radio frequency (RF), e.g., FM radio and/or digital audio broadcasting, an Institute of Electrical and Electronics Engineers (IEEE®) 802.11 standard (e.g., 802.11(g) or 802.11(n)), the Worldwide Interoperability for Microwave Access (WIMAX®) standard, a cellular phone technology (e.g., the Global Standard for Mobile communication (GSM)), a satellite communication link, and/or any other suitable communication means. More specifically, in the exemplary embodiment, data management system 108 transmits the data for the subjects to computing device 104. While the data is shown as being stored in database 110 within data management system 108, it should be noted that the data of the subjects may be stored in another system and/or device. For example, computing device 104 may store the data therein.

During operation, while the subject is in a resting state, sensing system 102 uses a magnetic field to align the magnetization of some atoms in the brain of the subject and radio frequency fields to systematically alter the alignment of this magnetization. As such, rotating magnetic fields are produced and are detectable by a scanner (not shown) within sensing system 102. More specifically, in the exemplary embodiment, sensing system 102 detects a plurality of measurements of brain activity that is representative of at least one parameter of the brain of the subject during the resting state. Sensing system 102 also generates at least one spectroscopic signal representative of the plurality of measurements and transmits the signal(s) to computing device 104 via data conduit 106. Moreover, data of other subjects may be transmitted to computing device 104 from database 110 via network 109. As explained in more detail below, computing device 104 produces at least one map, such as a functional connectivity map, for each of the measurements based on a comparison of at least one resting state data point of the subject and a corresponding data point from the previously acquired data set from at least one other subject. Computing device 104 uses the map to categorize or classify the brain activity in a plurality of networks in the brain.

FIG. 2 is a block diagram of computing device 104. In the exemplary embodiment, computing device 104 includes a user interface 204 that receives at least one input from a user, such as an operator of sensing system 102 (shown in FIG. 1). User interface 204 may include a keyboard 206 that enables the user to input pertinent information. User interface 204 may also include, for example, a pointing device, a mouse, a stylus, a touch sensitive panel (e.g., a touch pad, a touch screen), a gyroscope, an accelerometer, a position detector, and/or an audio input interface (e.g., including a microphone).

Moreover, in the exemplary embodiment, computing device 104 includes a presentation interface 207 that presents information, such as input events and/or validation results, to the user. Presentation interface 207 may also include a display adapter 208 that is coupled to at least one display device 210. More specifically, in the exemplary embodiment, display device 210 may be a visual display device, such as a cathode ray tube (CRT), a liquid crystal display (LCD), an organic LED (OLED) display, and/or an “electronic ink” display. Alternatively, presentation interface 207 may include an audio output device (e.g., an audio adapter and/or a speaker) and/or a printer.

Computing device 104 also includes a processor 214 and a memory device 218. Processor 214 is coupled to user interface 204, presentation interface 207, and to memory device 218 via a system bus 220. In the exemplary embodiment, processor 214 communicates with the user, such as by prompting the user via presentation interface 207 and/or by receiving user inputs via user interface 204. The term “processor” refers generally to any programmable system including systems and microcontrollers, reduced instruction set circuits (RISC), application specific integrated circuits (ASIC), programmable logic circuits (PLC), and any other circuit or processor capable of executing the functions described herein. The above examples are exemplary only, and thus are not intended to limit in any way the definition and/or meaning of the term “processor.”

In the exemplary embodiment, memory device 218 includes one or more devices that enable information, such as executable instructions and/or other data, to be stored and retrieved. Moreover, memory device 218 includes one or more computer readable media, such as, without limitation, dynamic random access memory (DRAM), static random access memory (SRAM), a solid state disk, and/or a hard disk. In the exemplary embodiment, memory device 218 stores, without limitation, application source code, application object code, configuration data, additional input events, application states, assertion statements, validation results, and/or any other type of data. Computing device 104, in the exemplary embodiment, may also include a communication interface 230 that is coupled to processor 214 via system bus 220. Moreover, communication interface 230 is communicatively coupled to sensing system 102 and to data management system 108 (shown in FIG. 1).

In the exemplary embodiment, processor 214 may be programmed by encoding an operation using one or more executable instructions and providing the executable instructions in memory device 218. In the exemplary embodiment, processor 214 is programmed to select a plurality of measurements that are received from sensing system 102 of brain activity that is representative of at least one parameter of the brain of the subject during a resting state. The plurality of measurements may include, for example, a plurality of voxels of at least one image of the subject's brain, wherein the image may be generated by processor 214 within computing device 104. The image may also be generated by an imaging device (not shown) that may be coupled to computing device 104 and sensing system 102, wherein the imaging device may generate the image based on the data received from sensing system 102 and then the imaging device may transmit the image to computing device 104 for storage within memory device 218. Alternatively, the plurality of measurements may include any other type measurement of brain activity that enables system 100 to function as described herein.

Processor 214 may also be programmed to perform a correlation analysis. More specifically, in the exemplary embodiment, processor 214 may be programmed to compare at least one data point from each of the plurality of measurements with a corresponding data point from a previously acquired data set from at least one other subject. For example, processor 214 may be programmed to compare a resting state data point from each selected voxel from an image of the subject with a corresponding data point that is located within the same voxel of the previously acquired data set of the other subject. Processor 214 may also be programmed to produce at least one map (not shown in FIG. 2) of the brain of the subject, such as a functional connectivity map, for each of the plurality measurements. The map is based on the comparison of the resting state data point and the corresponding previously acquired data point. The map, for example, may illustrate the location within the brain of a measured brain activity. Processor 214 may be programmed to produce the map by using the various compared data points in a known algorithm to calculate a plurality of outputs, such as, for example, at least one output vector. One algorithm that may be used is represented in Equation 1 below.

$\begin{matrix} {{input}_{1} = {{\tanh^{- 1}\left( {\left\lbrack \frac{\ln\left( {{1\text{/}{output}} - 1} \right.}{- a} \right\rbrack \cdot \frac{{pinv}\mspace{14mu}\left( {Weights}_{{hidden}\text{-}{output}} \right)}{a}} \right)} \cdot \frac{{pinv}\mspace{14mu}\left( {Weights}_{{input}\text{-}{hidden}} \right)}{b}}} & \left( {{Eq}.\mspace{14mu} 1} \right) \end{matrix}$

In Equation 1, a and b represent activating function parameters. The output represents a seven dimensional output vector and pinv represents a pseudo inverse function.

Processor 214 may also be programmed to categorize or classify the measured brain activity in a plurality of networks in the brain based on the map. For example, processor 214 may be programmed to categorize the measured brain activity to a particular neural network of the brain of the subject based on the location of the measured brain activity on the map of the subject's brain.

During operation, as the subject is in a resting state, sensing system 102 detects a plurality of measurements of brain activity that is representative of at least one parameter of the brain of the subject. Sensing system 102 transmits at least one signal representative of the measurements to computing device 104 via data conduit 106. More specifically, the signals are transmitted to and received by communication interface 230 within computing device 104. Communication interface 230 then transmits the signals to processor 214 for processing and/or to memory device 218, wherein the data may be stored and transmitted to processor 214 at a later time. Processor 214 may generate an image of the plurality of measurements. Alternatively, sensing system 102 may transmit the signals to an imaging device (not shown), wherein an image of the measurements may be generated. The image may then be transmitted to computing device 104, wherein the image is stored within memory device 218 and transmitted to processor 214 for processing.

Moreover, data of other subjects may be transmitted to computing device 104 from database 110 (shown in FIG. 1) via network 109 (shown in FIG. 1). More specifically, the data may be received by communication interface 230 and then transmitted to processor 214 for processing and/or to memory device 218, wherein the data may be stored and transmitted to processor 214 at a later time. Computing device 104 may obtain the data at any time during operation.

In the exemplary embodiment, computing device 104 produces at least one map for each of the plurality of measurements received. More specifically, processor 214 first selects each of the plurality of measurements, received from sensing system 102. For example, in the exemplary embodiment, processor 214 selects each of the voxels from the image. Alternatively, processor 214 may select any other types of measurements for brain activity that enables system 100 to function as described herein. Moreover, a user may see the image on the computing device 104, via presentation interface 207, and select the measurements, such as voxels, via user interface 204.

When each of the measurements has been selected, processor 214 then performs a correlation analysis. More specifically, processor 214 compares at least one data point from each of the selected measurements with a corresponding data point from a previously acquired data set from at least one other subject, wherein computing device 104 obtained the data set from database 110. For example, processor 214 may compare at least one resting state data point from each selected voxel of the image of the subject with a data point that is located within the same voxel of the previously acquired data set of at least one other subject.

When processor 214 has completed the correlation analysis, processor 214 then produces at least one map (not shown in FIG. 2) of the brain of the subject, such as a functional connectivity map, for each of the measurements. More specifically, processor 214 produces a map of the brain of the subject based on each of the comparisons of each of the resting state data points and the corresponding previously acquired data points. The map, for example, may illustrate the location within the brain of a measured brain activity. Processor 214 then categorizes or classifies the measured brain activity in a plurality of networks in the brain based on the map. For example, based on the location of the measured brain activity in the map, processor 214 categorizes the measured brain activity to a particular neural network of the brain of the subject. The map may be presented to the user via presentation interface 207. Moreover, a textual representation and/or a graphical output for the various categorizations may also be presented to the user via presentation interface 207.

FIG. 3 is flow diagram of an exemplary method 300 for task-less mapping of brain activity of a brain of a subject using system 100 (shown in FIG. 1). A sensing system 102 (shown in FIG. 1) detects 302 a plurality of measurements of brain activity that is representative of at least one parameter of the brain of the subject during a resting state. Sensing system 102 transmits 304 at least one signal representative of the measurements to a computing device 104 (shown in FIGS. 1 and 2). The signals are received 306 by a communication interface 230 (shown in FIG. 2). The measurements are selected 308 by a processor 214 (shown in FIG. 2). At least one data point from each of the measurements is compared 310 with a corresponding data point from a previously acquired data set from at least one other subject.

At least one map for each of the measurements is produced 312 based on the comparison of the resting state data point and the corresponding previously acquired data point. The brain activity is categorized 314 in a plurality of networks in the brain based on the map. The map and/or an output for the categorization are displayed 316 to a user, via a presentation interface 207 (shown in FIG. 2).

The embodiments of the system and method for task-less mapping of brain activity using resting state data of a brain of a subject, as described herein, were used in the following exemplary experiment.

Experiment

In the exemplary experiment, perceptron training and testing used data sets previously acquired at the Neuroimaging Laboratories at the Washington University School of Medicine in St. Louis, Mo. All patients were young adults screened to exclude neurological impairment and psychotropic medications. Demographic information and acquisition parameters are given in Table 1 below.

TABLE 1 Characteristics of the training test and validation data sets. Dataset Training Optimization (Test) Validation Number of 21 (7M + 14 F) 17 (8M + 9F) 10 (4M + 6F) Subjects Age 27.6 (23-35) years 23.1 (18-27) years 23.3 ± 3 years Number 128 × 6 runs 194 × 4 runs 100 × 9 runs of frames TR (s) 2.16 2.16 3.03* *The TR in the validation data set includes a one second pause between frames to accommodate simultaneous EEG recording.

In the exemplary experiment, all imaging was performed with a 3T Allegra scanner. Functional images were acquired using a BOLD contrast sensitive gradient echo echo-planar sequence [FOV=256 mm, flip angle=90°, 4 mm³ voxels, other parameters listed in Table 1] during which subjects were instructed to fixate on a visual cross-hair, remain still, and not fall asleep. Anatomical imaging included one sagittal T1-weighted magnetization prepared rapid gradient echo (MP-RAGE) scan (T1W) and one T2-weighted scan (T2W).

Initial fMRI preprocessing followed conventional practice known in the art. This included compensation for slice-dependent time shifts, elimination of systematic odd-even slice intensity differences due to interleaved acquisition, and rigid body correction for head movement within and across runs. Atlas transformation was achieved by composition of affine transforms connecting the fMRI volumes with the T2W and T1W structural images. Head movement correction was included with the atlas transformation in a single resampling that generated volumetric time series in 3 mm³ atlas space. Additional preprocessing in preparation for correlation mapping included spatial smoothing (6 mm FWHM Gaussian blur in each direction), voxel-wise removal of linear trends over each fMRI run, and temporal low-pass filtering retaining frequencies below 0.1 Hz.

Spurious variance was reduced by regression of nuisance waveforms derived from head motion correction and timeseries extracted from regions (of “non-interest”) in white matter and CSF. Nuisance regressors included also the BOLD timeseries averaged over the brain, i.e., global signal regression (GSR). Thus, all computed correlations were effectively order 1 partial correlations controlling for variance shared across the brain. GSR has been criticized on the grounds that it artificially generates anticorrelations. However, GSR fits well as a step preceding principal component analysis because it generates approximately zero-centered correlation distributions. As well, GSR enhances the spatial specificity in subcortical seed regions and reduces structured noise. The question of whether the left tail of a zero-centered correlation distribution (“anticorrelations”) is “false” or “tenuously interpretable” is irrelevant in the context of RSN classification.

Correlation maps were computed using standard seed-based procedures, i.e., by correlating the timeseries averaged over all voxels within the seed (generally, 5 mm spheres) against all other voxels, excluding the first 5 (pre-magnetization steady-state) frames of each fMRI run. Frame-censoring was employed with a threshold of 0.5% RMS frame-to-frame intensity change. Frame-censoring excluded 3.8±1.1% of all magnetization steady-state frames from the correlation mapping computations. Correlation maps were Fisher z-transformed prior to further analyses.

In the exemplary embodiment, Cortical reconstruction and volume segmentation were performed using FreeSurfer. Adequate segmentation was verified by inspection of the FreeSurfer-generated results in each of the 21 training and 17 test datasets. Cortical and subcortical gray matter regions were selected from these segmentations, thresholded to obtain a conjunction of 30% of subjects, and then masked with an image of the average BOLD signal intensity across all subjects, thresholded at 80% of the mode value. This last step removes from consideration brain areas in which the BOLD signal is unreliable because of susceptibility artifacts. The resulting 30,981 voxels constituted the grey matter mask. This mask was applied to all correlation maps input to the classifier. Individual surfaces were deformed to a common space, producing consistent assignment of surface vertex indices with respect to gyral features across subjects. Final volumetric results for each subject were sampled onto surface vertices by cubic spline interpolation onto mid-thickness cortical surface coordinates.

Seed regions were generated by meta-analyses of task-fMRI studies. Task-response foci were initially assigned to one of 10 functional networks in Table 2 below. Each task fMRI study contributed a variable number of foci (Task ROIs column in Table 2). Task foci were used as seeds to generate correlation maps in all 21 subjects in the training set. These maps then were entered into random effects analyses (against the null hypothesis of no correlation) to produce Gaussianized t-statistic (Z-score) images. Z-score images representing seeds assigned to the same RSN were averaged. Additionally, a conjunction image representing at least 70% of random effects images for a given network (after thresholding at |Z|>3) was produced. Averaged Z-score images were masked to include only voxels contained in the conjunction. Peaks of the conjunction-masked average were selected as center coordinates for 6 mm spherical ROIs. Accordingly, the constraint employed was that all ROIs within a given network must be separated by at least 12 mm. This process resulted in a large set of ROIs that were operationally treated as provisional.

TABLE 2 Studies of functional co-activation used to generate seed ROIs. Pro- Final Task visional seed RSN Task paradigm fMRI contrast ROIs seed ROIs ROIs DAN 1. Rapid Serial 1. Cue Type × 10 28 28 Visual Presentation event time (RSVP) 2. Cue location × 2. Rapid Serial cue type × event Visual Presentation time (RSVP) 3. Event time 3. Posner Cueing Task VAN Posner Cueing Task Invalid vs. Valid 2 19 15 CO* Mixed design (10 Graph theoretic N/A 7 different tasks) analysis* SMN Posner Cueing Task Target Period 11 37 39 AN Various auditory Stimulation vs. 2 12 stimuli control VIS Visual Localizer Peripheral 8 19 30 Foveal 2 12 FPC* Mixed design (10 Graph theoretic N/A 11 12 different tasks) analysis* LAN Perceptual vs. Sentence 13 17 13 Episodic Memory Reading Search Paradigm DMN Perceptual vs. Memory 4 42 32 Episodic Memory Retrieval Search Paradigm *Regions reported were themselves the result of a meta-analysis followed by refinement. Hence, these seeds were directly used as provisional ROIs.

In the exemplary embodiment, the provisional ROI set was iteratively refined by maximizing the spatial concordance between the correlation map obtained from each seed and the map obtained by pooling all seeds within the RSN to which the seed was assigned. Pooled seed correlation maps were computed by averaging the time series across all seeds assigned to each RSN. The single seed and the pooled seed maps were averaged across subjects. RSN concordance was assessed as the spatial correlation between the (subject-averaged) single seed and the (subject-averaged) pooled seed maps. Seeds were considered outliers if their concordance estimate was less than 1.5 times the inter-quartile range below the median of all other seeds in the RSN. Outlier seeds were reassigned to the RSN of greatest concordance, unless they were maximally concordant with the currently assigned RSN, in which case they were removed entirely. After reassignment and outlier rejection, new individual seed and pooled seed correlation maps were re-computed and the process was iterated. Convergence (no reassignments or outlier rejections) was achieved in 7 iterations. The cingulo-opercular (CO) network did not survive iterative refinement, and most seeds were reassigned to the ventral attention network or removed. Similarly, the auditory network was subsumed into the sensorimotor network and the originally distinct foveal and peripheral visual networks were combined into a single (VIS) network.

Iterative refinement yielded 169 ROIs representing 7 RSNs with high intra- and low inter-network correlation, as shown in FIGS. 4 and 15. To these were added a nuisance category consisting of 6 ROIs in CSF spaces. The latter enabled the classifier to separate correlation patterns representing CSF vs. true RSNs. Computing correlation maps for each of the 175 seed regions in all 21 training subjects produced 3,675 images that were used as training data. Each image in the training set was masked to include only grey matter voxels, producing a 3,675×30,981 matrix. Similarly, 175·17=2,975 images were computed in the test data set. Each image was assigned to one RSN (see the description of iterative seed ROI refinement above and Table 2).

A multilayer perceptron was constructed to classify resting-state fMRI correlation maps into 7 canonical spatial patterns predefined as resting-state networks. The core of the perceptron is an artificial neural network that includes an input, hidden, and output layer, each consisting of some number of nodes fully connecting to the next layer (all-to-all feed-forward). Training samples (correlation maps from a particular seed and subject) are passed into this feed-forward network and the output is compared to the correct RSN label, as specified in the fMRI task meta-analysis. The error in this comparison is used to update the connections, or weights, between layers to increase the performance of the classifier.

As an initial pre-processing step, the dimensionality of the input data was reduced by using principal component analysis (PCA). Representing correlation maps in terms of eigenvectors provides efficient computation, well-conditioned weight matrices, and a free parameter to represent the complexity of the input data (number of PCs). PCA was performed on the matrix of masked correlation images (21 subjects×175 seeds=3,675 images×30,981 voxels for PCA). Each correlation map in the training (3,675 images) and the test (2,975 images) data sets were then represented using a variable number of principal components (PCs).

The input layer received the correlation map training data as vectors in PCA space (the value of a given correlation map projected along a particular PC). Thus, the number of input nodes was a free parameter that depended on the number of PCs used to represent the data. Each training example (a correlation map from a particular seed ROI/subject pair) was associated with a desired output value, d_(o) (Eq. (7)), corresponding to the a priori RSN labels. The goal of the training process is to compare the output to these desired values, thereby generating an error signal used to update connection weights. The overall transfer function of the perceptron (Eq. (2)) corresponds to the detailed schematic of the propagation of inputs through the perceptron (FIG. 5, see legend for symbol definitions).

$\begin{matrix} {y_{o} = {\varphi_{o}\left( {\sum\limits_{h}{{w_{ho}(\eta)} \cdot {\varphi_{h}\left( {\sum\limits_{i}{w_{ih} \cdot y_{i}}} \right)}}} \right)}} & (2) \end{matrix}$

The total input to each hidden node, v_(h), is determined by the sum of all input nodes, weighted by the feed-forward connections (Eq. (3)). This sum is then transformed by the hidden layer activation function to compute the output value of the hidden layer node, y_(h) (Eq. (4)).

$\begin{matrix} {v_{h} = {\sum\limits_{i}{w_{ih} \cdot y_{i}}}} & (3) \\ {y_{h} = {{\varphi_{h}\left( v_{h} \right)} = {a \cdot {\tanh\left( {b \cdot v_{h}} \right)}}}} & (4) \end{matrix}$

The output layer nodes operate in the same manner as hidden layer nodes (Eqs. (5) and (6)):

$\begin{matrix} {{v_{o}(n)} = {\sum\limits_{i}{{w_{ho}(n)} \cdot {y_{h}(n)}}}} & (5) \\ {y_{o} = {{\varphi_{o}\left( v_{o} \right)} = \frac{1}{1 + e^{{- a} \cdot v_{o}}}}} & (6) \end{matrix}$

The After propagation of the input data through the perceptron, the output value for each node, y_(o), was compared to the desired value, d_(o), to find the error, e_(o) (Eq. (7)). e _(o)(k)=d _(o)(k)−y _(o)(k)   (7)

The local gradient of the error at an output node is found by the product of this error and the inverse of the activating function applied to the output value: δ_(o) =e _(o)·φ′_(o)(v _(o))   (8) where the prime notation indicates the first derivative. After every iteration (n), the weights for the hidden to output layer connections were adjusted in the direction opposite of the gradient of the error: w _(ho)(k+1)=w _(ho)(k)+η(k)·δ_(o)(k)·y _(h)(k)   (9) where η is the learning rate, y_(h) is the value of hidden layer node h, and δ_(o) is local error gradient at output node o. Similarly, the weights to the hidden layer from the input layer, w_(ih), are adjusted according to Eq. (10). w _(ih)(k+1)=w _(ih)(k)+η(k)·δ_(h)(k)·y _(i)(k)   (10)

The local gradient at a hidden node, δ_(h), may be computed by back-propagation from the output layer.

$\begin{matrix} {\delta_{h} = {{- \frac{dE}{d\;\gamma_{h}}} = {{\varphi_{h}^{\prime}\left( v_{h} \right)} \cdot {\sum\limits_{o}{\delta_{o} \cdot w_{ho}}}}}} & (11) \end{matrix}$

The learning rate parameter, η, was set empirically. A range of stable values was determined for a constant η, where instability was noted as divergence or rapid oscillation of classifier weights. The present results were obtained using an adaptive learning rate that increased as a sigmoid in log iteration index (FIG. 6). The initial learning rate was small (η(0)=A=5·10⁻⁴) to allow the classifier to begin a gentle descent in error gradient towards a stable solution. The learning rate increased exponentially (B=−3, Q=0.5), until saturating at an empirically determined upper limit of stability to (η(∞)=2·10⁻³).

Separation of classes was quantified using receiver operator characteristic analysis. Across a range of thresholds, the proportion of within-class output values above the threshold (true positive fraction, TPF) were compared to the number of out-of-class values above the threshold (false positive fraction, FPF). The TPF as a function of FPF defines the ROC curve. The area under the ROC curve (AUC) was used as a summary statistic of classification performance for each RSN class.

At logarithmically spaced intervals during the training process, training was paused and AUCs were calculated in a separate test data set. This procedure produced training trajectories indicating the relative performance for each RSN (FIG. 9D) throughout the training process. Peak performance for a given RSN was defined at the iteration producing the maximum AUC value in the test data (FIG. 9D). Overall performance was calculated as the average of AUC values across networks (FIG. 9D).

In the exemplary embodiment, the number of PCs sampled (N_(i)), and the number of nodes in the hidden layer (N_(h)) constitute hyper-parameters subject to optimization. Overall RMS error was evaluated over a densely sampled N_(i)∈[5, 6600]×N_(h)∈[4, 5000] space. For each (N_(i), N_(h)) coordinate, a classifier was trained until test set error reached a minimum. The architecture with the least error (minimum of eight repetitions for each coordinate) was selected (FIG. 10).

After identifying the architecture with least error in the test data set, performance was further optimized by simulated annealing, countering the tendency of perceptrons to become trapped in local minima. Mimicking the random movement of atoms aligning in cooling metal, simulated annealing uses random perturbations of model parameters to find the global extremum in an objective function. Perturbations of steadily decreasing size (specified by a ‘cooling profile’) are guaranteed to find a global minimum with slow enough cooling, although, in practice, the necessary cooling profile is prohibitively slow. After training the perceptron until a minimum in RMS test set error, every weight, {w_(ih)} and {w_(ho)}, was multiplied by a random coefficient. Training was then resumed to find a new minimum. If lower error was achieved, the new weights were accepted. This process was then repeated.

The value for each weight was determined by first sampling from a uniform distribution, x∈[−1, 1], transformed by a hyperbolic function, N=(1−x)/(1+x). Thus each weight was multiplied by N∈[0,∞], and was thus unchanged when x=0. The range sampled within x determined the amount of noise injected into the system, using values closer to zero over the course of cooling. The maximum value of x was determined by the temperature, T, and the minimum value was determined so that the mean squared value of N was unity:

$\begin{matrix} {{\frac{1}{T - a}{\int_{a}^{T}{\left( \frac{x - 1}{x + 1} \right)^{2}{dx}}}} = 1} & (12) \end{matrix}$

This choice of noise ensured that the sum of squares of the connection weights was unaltered by perturbation and that most weights were decreased, while a small selection was sporadically increased. A geometric cooling function (Eq. (13)) was used, which decreased over K₁ perturbation epochs; this entire annealing process was repeated K₂ times, each time with a slightly cooler temperature profile. T _(k) _(1,) _(k) ₂ =T ₀ ·r ^((k) ¹ ^(+3k) ² ⁾   (13)

The following parameters were used: r=0.95, T₀=0.4, K₁=40, K₂=20. To map RSNs in individual subjects, a correlation map was generated for every voxel in the brain and then classified using the trained and optimized perceptron. An overall schematic of this process is depicted in FIG. 10. A correlation map was produced for every point in the brain by correlating every voxel's BOLD time-course with every other voxel in the brain. Each map was masked before classification to include only grey matter voxels producing a 65,549 (voxels within brain mask)×30,981 (voxels within grey matter mask) element matrix (FIG. 7). This data was then projected onto the eigenvectors of the training data, reducing the dimensionality to 65,549×2500 (FIG. 7). Thus, all correlation maps were represented in the same input data space for classifier training and testing. The reduced whole-brain connectivity data was then propagated through the perceptron, with the first layer reducing the data to 22 features (65,549×22; FIG. 7), and the second layer producing RSN estimates (65,549×8, FIG. 7). However, FIG. 7 depicts only 7 output classes because one of the 8 outputs is a nuisance component used only in post-processing.

Classifier output values are approximately uniformly [0,1] distributed as a result of the logistic activation function on the output layer (Eq. (6)). Classifier values were then normalized within each voxel to sum to unity (FIG. 7). This normalization penalized voxels that had high classification values for multiple networks. The presence of a CSF classification component further penalized RSN estimates in voxels exhibiting CSF-related correlation patterns. Within each network, classifier values were then converted back to an exactly uniform [0,1] distribution across voxels (rank-order transform). This transformation resulted in voxels ranked in membership for each network across the brain expressed as a percentile.

To visualize group level results on the cortical surface, RSN topography estimates were projected to the cortical mid-thickness surface for each subject (after surface-registration across subjects). Averages were then computed across surface nodes. The standard deviation of classifier values was also calculated node-wise to illustrate regions of high variability. These group-level results were projected onto the group-average inflated surface. To visualize group level results in sub-cortical structures, classifier values were averaged voxel-wise across subjects. Group-average images were then re-sampled to 1 mm cubic voxels and overlaid on a co-registered MNI152 atlas target.

In the exemplary embodiment, spatial correlation analysis (FIG. 8B) and principal component analysis (FIG. 8C) of the training data (the correlation maps produced for each seed ROI) revealed distinct clustering corresponding to RSNs. In the map-to-map spatial correlation matrix (averaged across subjects), training inputs showed high correlation with other inputs of the same RSN compared to inputs of other classes (FIG. 8B). Additionally, the map-to-map correlation matrix showed two major clusters, one corresponding to the DAN, VAN, VIS, and MOT networks, and the other corresponding to the FPC, LAN, and DMN networks. Projection of all 3,675 correlation maps into principal component space gave rise to partially overlapping clusters corresponding to 7 RSNs. In the PC1×PC2 plane (FIG. 6), DAN (purple) and DMN (red) showed little overlap and appeared at opposite ends of the PC1 axis. MOT (light blue) and VIS (green) clusters were highly overlapping in this plane, but showed little overlap in the PC3×PC4 plane.

FIGS. 9A-9F shows the training performance for the perceptron optimized for overall performance (2500 input PCs, 22 hidden layer nodes). For every correlation map, the perceptron output node values represent an estimate of membership for each RSN. The expectation value of all initial perceptron outputs is 0.5 (FIGS. 9A-9C) as the expected output value with zero-mean weights (v_(o)) is 0 (Eqs. (5) and (6)). As training progresses, within-class output values increase towards unity (e.g., DMN output node values for DMN inputs, red traces in FIG. 9A), while out-of-class output values decrease towards zero (DMN output value for non-DMN ROI-derived maps, all other traces in FIG. 9A).

Area under the ROC curve (AUC) trajectories are shown in FIG. 9D. This quantity, averaged across RSN classes, began near chance (0.65 after one iteration) and rose in later iterations. For all networks, the AUC exhibited a transient decrement in performance early in training. This feature corresponded to transient changes of slope in RMS error but did not produce concavity (local minima) in RMS error (FIG. 9E). Class separation was achieved at varying numbers of iterations for different RSNs. Across all perceptron architectures, the default mode network (red trace) always achieved asymptotic performance earliest, and the language network (orange) latest. Asymptotic performance for CSF classification occurred much later than any true RSN. Performance on the test data initially followed training performance until reaching a global maximum (FIG. 9E). This maximum occurred at varying iteration indices for different RSNs. Training beyond this point resulted in over-fitting, manifesting as decreasing test data performance despite increasing training performance. Inputs that were previously correctly classified in the test data became incorrectly classified (FIGS. 9B and 9C).

Over a dense sampling of input and hidden layer sizes (N_(i)×N_(h)), the perceptron was trained until the peak AUC could be determined (FIG. 10). The optimal overall performance for the perceptron was found at 2500 PCs and 22 hidden layer nodes (FIG. 10). The perceptron was trained with this architecture using 10 mm ROIs and the result was optimized through simulated annealing, yielding an over-all classification performance of 0.9822 (AUC) with 17.1% RMS error. The maximal AUC and minimal RMS error rates differed by network, as shown in Table 3.

TABLE 3 RSN classification performance. Test Retest (Validation (Optimization Set) Set) Accuracy Error Accuracy Error Network (AUC) (RMS) (AUC) (RMS) DAN 0.973 20.2% 0.973 20.1% VAN 0.971 17.9% 0.979 17.6% SMN 0.988 16.4% 0.994 17.2% VIS 0.993 13.4% 0.998 12.7% FPC 0.972 17.5% 0.989 14.8% LAN 0.985 14.9% 0.991 14.4% DMN 0.993 14.4% 0.990 17.6% Mean 0.982 17.1% 0.988 16.6%

These values reflect MLP training with 10 mm radius seeds (FIGS. 14A and 14B) and optimization with simulated annealing. After completion of classifier training, voxel-wise connectivity patterns were classified in individual subjects in the test data set (FIG. 11A). RSN topography summaries were computed as winner-take-all maps (FIGS. 11A-11F and 12A-12C). Well-defined RSN topography was obtained in all subjects in the test and training groups. Specifically, the subject-wise mean and standard error of the AUC was 0.982±0.007, with the worst performing subject at 0.963. These figures corresponded to RMS error of 16.5±1.4% with the worst subject yielding 19.1%. RSNs were generally contiguous regions that conformed to previously described topography. The relationship of perceptron-defined RSNs to previous findings is discussed more fully below.

RSN topography estimates were averaged over all subjects in the training and test groups. FIGS. 12A-12C addresses both the central tendency (top row) of each group as well as inter-subject variability (middle row). Average network topographies had higher values near locations of ROIs used to generate training maps. This is expected because voxels within ROIs are likely to have similar correlation maps to the ROI. High classification values (in the top 25%) were also found in contiguous regions not used to generate training data. For example, a lateral temporal region was classified as a fronto-parietal control region, and a dorsal pre-motor region was classified into the language network. This type of result demonstrates external validity (or equivalently, generalizability) of perceptron classification, i.e., recovery of true features in RSNs not included in the training set. These features are also present in the results in individual subjects (FIG. 11A).

Further evidence of external validity is shown in FIG. 13. For example, thalamic voxels approximately corresponding to nucleus ventralis posterior were classified as SMN, substantially in agreement. Similarly, voxels in the posterior cerebellum (Crus I and II) and the cerebellar tonsils were classified as DMN (FIG. 13, Z=−30, Z=−47), substantially in agreement. These results are notable because neither cerebellar nor thalamic ROIs were used to generate training data. Further, no cerebellar voxels were within the grey matter mask, which means that the classifier successfully identified cerebellar ROIs purely on the basis of cortical connectivity maps. The perceptron generated asymmetric classification in the cerebellum for the VAN and LAN networks (Z=−30), contralateral to their asymmetric cerebral representation (Z =+47).

The present results (individual and group RSN topographies) exhibit a high degree of face validity with respect to the training data and previously reported RSN results (FIGS. 11A-11F, 12A-12C, and 13). Thus, for example, components of the DMN used as seeds to generate the training data were classified as DMN in all subjects. This was true not only for easily classified networks (e.g., the DMN) but also for networks (e.g., VAN and LAN) that are inconsistently found by unsupervised procedures. The results shown in FIGS. 11A-11F illustrate that the perceptron reliably classified RSNs in each test set individual (AUC>0.971), even in cases in which the RMS error was relatively high (>0.2).

However, inter-individual differences were also evident (FIGS. 11A-11F). These differences systematically varied according to RSN and exhibited RSN-specific zones of high as well as low inter-individual variability (FIGS. 12A-12C). Easily classified voxels (i.e., with high classification values) generally showed the least inter-subject variability. Such regions, e.g., the posterior parietal component of the DMN (FIGS. 12A-12C), were surrounded by zones of high variability (e.g., ring around the angular gyrus). The pre-and post-central gyri consistently showed high SMN classification values but were bordered by regions of high inter-subject SMN variability. Interestingly, inter-subject variability was low also in areas with classification values near 0, particularly in areas typically anticorrelated with other networks (e.g., low DAN variance in the angular gyrus, a component of the DMN; low DMN variance in MT+, a component of the DAN).

At least four factors potentially contribute to observed inter-subject classifier output variability: (i) limited or compromised fMRI data, (ii) limitations intrinsic to the MLP (iii) true inter-individual differences in RSN topography and (iv) misregistration. Each of the possibilities is discussed below.

With regard to (i), the fMRI data used in the present work were obtained in healthy, cooperative young adults. Hence, the fraction of frames excluded because of head motion was low (about 4%). The total quantity of fMRI data acquired in each individual was, by current standards, generous. However, fMRI data quantity clearly affects MLP performance (see 4.4.2 below and FIGS. 14A-14B). Current results suggest that more data generally improves MLP performance. The impact of fMRI data quality and quantity on MLP performance in clinical applications remains to be determined. With regard to (ii), the observation of zones with high classifier values and low variance bordered by regions of high variance may reflect classification uncertainty in areas that truly represent more than one RSN, i.e., voxels with high participation coefficients.

With regard to (iii), on the other hand, the presently observed inter-individual differences may truly reflect individual variability in RSN topography. Previous work has demonstrated that inter-individual differences in task-evoked activity correspond to “transition zones” in resting state networks (e.g., the boundary between parietal DMN and DAN regions). These same regions appear in our inter-subject variance maps for both DMN and DAN (FIGS. 12A-12C). We also note that areas of high RSN classification variability (pre-frontal, parietal, lateral temporal) broadly correspond to regions exhibiting the greatest expansion over the course of human development and evolution. This correspondence may possibly be coincidental, but it is consistent with the hypothesis that later developing or evolutionarily more recent areas of the brain tend to be more variable across individuals.

With regard to (iv), some proportion of the variability in observed RSN topography estimates may be explained by uncorrected anatomical variability. To investigate this possibility, the overall RSN standard deviation map (FIG. 16A) was compared to sulcal depth variability (FIG. 16B) and a weak spatial correlation (r=0.2) was found. By inspection, these maps were concordant only at a broad spatial scale: both showed low variability in primary motor/auditory/insular cortices and high variability elsewhere. Little correspondence was evident at finer scales (note lack of annular patterns in FIG. 16B). The degree to which anatomical variability contributes to spurious variance in RSN topography estimates may be addressed by measuring the degree to which non-linear or surface-based registration decreases inter-subject variance and increases overall classifier performance (higher AUC, lower RMSE).

Two distinct types of external validity, that is, correct classification outside the training set, are evident in our results. First, high overall classification performance was achieved for a priori seed-based correlation maps in test (98.2% AUC) and hold-out datasets (98.8% AUC). Performance was reliable in all subjects (97.1% worst-case AUC), which is critical in clinical applications. Second, and perhaps of greater scientific interest, the RSN estimates in areas not covered by seed regions were strongly concordant with previously reported task-based and resting-state fMRI results. For example, while no temporal FPC seed ROI was included in the training set, a posterior temporal gyrus locus was classified as FPC the group level (FIGS. 12A-12C). Similarly, the MLP also identified the parahippocampal gyrus as DMN and a dorsal pre-motor region that has been associated with articulation of speech as LAN. The right inferior cerebellum was first associated with language function by PET studies of semantic association tasks. Identification of this region here as part of the LAN network (FIG. 13, WTA, Z<−30) is doubly significant. First, no cerebellar seeds were used to generate training data and, further, cerebellar voxels were excluded from the gray matter mask, hence, were not seen by the classifier. Second, lateralized RSN components typically are not found by unsupervised seed-based correlation mapping.

These findings highlight the capabilities of supervised classifiers applied to the problem of identifying RSNs in individuals. The representation of language (primarily Broca's and Wernicke's areas) has been extensively studied using task-based fMRI and correlation mapping with a priori selected ROIs. However, the language network, as presently defined, typically is not recovered as such by unsupervised methods. Rather, components of the LAN are generally found only at fine-scale RSN descriptions. Thus, an RSN including Broca's and Wernicke's areas appears as the 11th of 23 components in; these same areas were identified as VAN and DMN. A component consistent with the presently defined LAN at a hierarchical level of 11 (but not 7) clusters has been found. Thus, the exemplary experiment, work demonstrates the potential of supervised classifiers to find networks that are subtle features of the BOLD correlation structure, possibly even minor sub-components within hierarchically organized RSNs, that nevertheless have high scientific and/or clinical value. The LAN was specifically included here to meet the clinical imperative of localizing language function in the context of pre-operative neurosurgical planning.

In the exemplary embodiment, the hierarchical scale of an RSN is reflected in training performance trajectories (FIGS. 9E and F): in all (N_(i)×N_(h)) architecture variants, the DMN was the first to be separated from other RSNs. The DMN arguably is the most robust feature in the correlation structure of intrinsic brain activity. Its topography is very similar across RSN mapping strategies (specifically, spatial ICA and seed-based correlation mapping. Here, the DMN and regions anti-correlated with the DMN were well separated along the first principal component of the training data (FIG. 6).

After the DMN, the sensorimotor and visual networks were next to achieve separation during classifier training. These networks are often seen at the next level down in the RSN hierarchy as offshoots of the anti-DMN or extrinsic system. The dorsal attention network achieved only a small peak in error descent compared to other ‘extrinsic’ networks, though this occurred in close proximity (note overlap of DAN, MOT, VIS peaks in FIG. 9F). In contrast, the LAN and VAN were last to achieve separation during training. This corresponds to the observation that LAN and VAN systems are typically found by analyses extending to lower levels of the RSN hierarchy.

In the exemplary embodiment, the observer is a multi-layer perceptron and the task is to assign RSN labels to each voxel. Performance is evaluated in terms of mean squared classification error and ROC analyses. It follows that MLP performance can be used to evaluate image quality across a wide range of variables, e.g., scanners, and acquisition parameters (e.g., TR, run length, resolution), preprocessing strategies (nuisance regression, filtering, spatial smoothing) and data representations (surface or volume based). This principle is demonstrated by systematically evaluating MLP performance in relation to quantity of fMRI data and seed ROI size.

The relation between total quantity of fMRI data and MLP performance (test dataset RMS error) is shown in FIG. 14A. The plotted points represent five replicate MLP training/test runs. RMS error as a function of data quantity was well fit (R²=0.994) by a three-parameter empirically derived hyperbolic function. The parameterized function implies that classifier error monotonically decreases with increasing total fMRI data length but ultimately asymptotes at ˜18% RMS (with 5 mm radius seeds and no simulated annealing). The existence of this asymptote may indicate that resting-state brain networks are inherently non-separable in the sense of classification. This is consistent with the notion of “near decomposability” of hierarchical systems formed by multiple, sparsely inter-connected modules. This concept has since been extended to brain networks.

The relationship between seed ROI radius and RMS classification error was explored using a perceptron architecture optimized with 5 mm radius seeds (2500 PCs, 22 hidden nodes). All seeds were masked to include only gray matter voxels. The results of systematically varying seed ROI size are shown in FIG. 14B. A clear minimum in RMS error was obtained with seeds of approximately 10 mm radius. Voxel-wise RSN topographies were qualitatively similar across ROI sizes, but larger seeds generated less noisy RSNs with more pronounced peaks. This result is unexpected, as it deviates from the current standard practice of using approximately 6 mm radius seeds. There are several possible explanations for the present results. Lame seeds may best match the characteristic dimensions of RSNs in the 7-network level description of the brain. Alternatively, large seeds may compensate for mis-registration in affine-coregistered, volume-preprocessed data. Smaller seeds may be used in classifiers operating on non-linear, surface-coregistered, geodesically smoothed data. The results shown in FIG. 14B reflect the effect of seed radius on the correlation maps used to train the MLP. It is formally possible for a corrupted training set to yield a better classifier as evaluated by test set classification error. Thus, the results shown in FIG. 14B should not be interpreted as unambiguously indicating that 10 mm radius seeds are optimal for correlation mapping.

Inter-individual differences in computed RSN topographies may reflect multiple factors. Cross-gyral contamination due to the relatively large voxels used in this study (4 mm acquisition, 3 mm post-processing analyses) may limit the precision of RSN classification in the dataset. Potential strategies for validating perceptron-derived results include comparison with measures of structural (axonal) connectivity and invasive electrophysiologic recording.

The MLP RSN classifier operates at the voxel level via computed correlation maps. After training, it reliably identifies RSN topographies in individual subjects. Classification is rapid (2 minutes using Matlab running on Intel i7 processors) and automated, hence suitable for deployment in clinical environments. After training, classification is independent of any particular seed. Therefore, the trained MLP is expected to be robust to anatomical shifts and distortions, for example, owing to enlarged ventricles and mass effects or even loss of neural tissue (e.g., stroke).

In this experiment, the classifier was trained to operate in 3D image space for compatibility with clinical imaging formats. However, the MLP concept can be readily adapted to operate on correlation maps represented on the cortical surface. Similarly, voxel-wise classifiers can be trained to classify subjects despite anatomical abnormalities (e.g., brain tumors) by altering the domain of the training set, i.e., excluding tumor voxels. Another potentially useful MLP modification would be removal by regression of the relationship between correlation and distance to the seed. Such regression may decrease the reliance of the classifier on local connectivity, thereby reducing susceptibility to corruption by movement artifact.

As compared to known systems that are used for brain mapping, the embodiments described herein enable a substantially efficient task-less system for brain mapping. More specifically, the embodiments described herein include a computing device for use in a system for mapping brain activity of a subject that generally comprises a processor. The processor is programmed to select a plurality of measurements of brain activity that is representative of at least one parameter of a brain of the subject during a resting state. Moreover, the processor is programmed to compare at least one data point from each of the measurements with a corresponding data point from a previously acquired data set from at least one other subject. The processor is also programmed to produce at least one map for each of the measurements based on the comparison of the resting state data point and the corresponding previously acquired data point. The processor may also be programmed to categorize the brain activity in a plurality of networks in the brain based on the map. By using previously acquired data points to categorize the brain activity in a plurality if networks in the brain of the subject, task-based techniques may be avoided. Moreover, by having the processor select the plurality of measurements, a user may no longer need to spend a considerable amount of time determining which measurements, such as voxels, to select.

Exemplary embodiments of the system, apparatus, and method are described above in detail. The system, apparatus, and method are not limited to the specific embodiments described herein, but rather, components of the system and apparatus, and/or steps of the methods may be utilized independently and separately from other components and/or steps described herein. For example, but not limited to, the system may also be used in combination with other apparatus, systems, and methods, and is not limited to practice with only the system as described herein. Rather, the exemplary embodiment can be implemented and utilized in connection with many other applications.

Although specific features of various embodiments of the invention may be shown in some drawings and not in others, this is for convenience only. In accordance with the principles of the invention, any feature of a drawing may be referenced and/or claimed in combination with any feature of any other drawing.

Although described in connection with an exemplary computing system environment, embodiments of the invention are operational with numerous other general purpose or special purpose computing system environments or configurations. The computing system environment is not intended to suggest any limitation as to the scope of use or functionality of any aspect of the invention.

Embodiments of the invention may be described in the general context of computer-executable instructions, such as program modules, executed by one or more computers or other devices. The computer-executable instructions may be organized into one or more computer-executable components or modules. Generally, program modules include, but are not limited to, routines, programs, objects, components, and data structures that perform particular tasks or implement particular abstract data types. Aspects of the invention may be implemented with any number and organization of such components or modules. For example, aspects of the invention are not limited to the specific computer-executable instructions or the specific components or modules illustrated in the figures and described herein. Other embodiments of the invention may include different computer-executable instructions or components having more or less functionality than illustrated and described herein. Aspects of the invention may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.

In operation, a computer executes computer-executable instructions embodied in one or more computer-executable components stored on one or more computer-readable media to implement aspects of the invention described and/or illustrated herein.

The order of execution or performance of the operations in embodiments of the invention illustrated and described herein is not essential, unless otherwise specified. That is, the operations may be performed in any order, unless otherwise specified, and embodiments of the invention may include additional or fewer operations than those disclosed herein. For example, it is contemplated that executing or performing a particular operation before, contemporaneously with, or after another operation is within the scope of aspects of the invention.

When introducing elements of aspects of the invention or the embodiments thereof, the articles “a,” “an,” “the,” and “said” are intended to mean that there are one or more of the elements. The terms “comprising,” “including,” and “having” are intended to be inclusive and mean that there may be additional elements other than the listed elements.

This written description uses examples to disclose the invention, including the best mode, and also to enable any person skilled in the art to practice the invention, including making and using any devices or systems and performing any incorporated methods. The patentable scope of the invention is defined by the claims, and may include other examples that occur to those skilled in the art. Such other examples are intended to be within the scope of the claims if they have structural elements that do not differ from the literal language of the claims, or if they include equivalent structural elements with insubstantial differences from the literal language of the claims. 

What is claimed is:
 1. A computer-implemented method for mapping a resting brain activity of an individual subject at a plurality of positions within the brain of the individual subject, the method comprising: receiving, at a computing device, a plurality of correlation maps, each correlation map comprising a plurality of elements, each element comprising a correlation between one time-series measurement and an additional time-series measurement selected from of a plurality of time-series measurements, the one time-series measurement obtained from one location within the brain of the individual subject during a resting state, and the additional time-series measurement obtained from one of a plurality of additional locations within the brain of the individual subject during a resting state; transforming, using the computing device, each correlation map to a classification of brain activity using a predetermined supervised classifier; and mapping, using the computing device, each classification of brain activity transformed from each correlation map to the one location within the brain of the individual subject for each correlation map.
 2. The method of claim 1, wherein transforming each correlation map to a classification of brain activity further comprises using the predetermined supervised classifier consisting of a perceptron, the perceptron comprising a plurality of nodes arranged in an input node layer, a hidden node layer, and an output node layer, the perceptron characterized by the equation: y _(o)=φ_(o)[Σ_(h) w _(ho)·φ_(h)Σ_(i)(w _(ih) ·y _(i)))], wherein: y_(i) is a vector of input node values of the input node layer, each input node value corresponding to an element of each correlation map; y_(o)is a vector of output node values of the output node layer, each output node value comprising a probability of membership of each correlation map in each of a plurality of resting state networks; w_(ih) is a first pre-determined weighting matrix configured to transform the vector of input node values to a vector of hidden node input values; φ_(h)is a pre-determined hidden node layer activation function configured to transform the vector of hidden node input values to a vector of hidden node values; W_(ho) is a second pre-determined weighting matrix configured to transform the vector of hidden node values to a vector of output node input values; and φ_(o)is a pre-determined output node layer activation function configured to transform the vector of output node input values to the vector of output node values.
 3. The method of claim 2, further comprising: projecting, using the computing device, each correlation map onto a plurality of predetermined principal components to produce a vector in a predetermined PCA space; and assigning, using the computing device, the vector in the predetermined PCA space to the vector of input node values.
 4. The method of claim 3, further comprising, receiving, at the computing device, a plurality of parameters defining the predetermined supervised classifier, the plurality of parameters comprising w_(ih), φ_(h), w_(ho), and φ_(o).
 5. The method of claim 4, wherein φ_(h) is defined by the equation: φ_(h)(v _(h))=a·tan h(b·v _(h)), wherein: v_(h) is the vector of hidden node input values, a is a first predetermined constant, and b is a second predetermined constant.
 6. The method of claim 5, wherein φ_(o) is a logistic activation function defined by the equation: ${{\varphi_{o}\left( v_{o} \right)} = \frac{1}{1 + e^{- {av}_{o}}}},$ wherein: v_(o) is the vector of output node input values, and a is a predetermined constant.
 7. The method of claim 6, wherein the input node layer comprises a plurality of input nodes, each input node configured to receive one value of the vector in the predetermined PCA space corresponding to one of the plurality of principal components.
 8. The method of claim 7, further comprising performing, using the computing device, a principal components analysis on a training data set to obtain the plurality of predetermined principal components, the training data set comprising a plurality of correlation maps corresponding to a plurality of positions within a plurality of brains of a plurality of training subjects.
 9. The method of claim 8, wherein the input node layer comprises a plurality of input nodes configured to receive the vector of input node values, wherein the number of input nodes ranges from about 10 to about 10,000.
 10. The method of claim 9, wherein the hidden node layer comprises a plurality of hidden nodes, each hidden node fully connected to the plurality of input nodes by a plurality of weighted feed-forward connections, the plurality of weighted feed-forward connections characterized by the first pre-determined weighting matrix, wherein the number of hidden nodes ranges from about 10 to about 10,000.
 11. The method of claim 10, wherein the output node layer comprises a plurality of output nodes, each output node fully connected to the plurality of hidden nodes by a second plurality of weighted feed-forward connections, the second plurality of weighted feed-forward connections characterized by the second pre-determined weighting matrix, wherein the number of output nodes ranges from about 5 to about
 100. 12. The method of claim 11, wherein the plurality of output nodes comprises a group of classification nodes and a nuisance node, each classification node configured to receive the probability of membership in one of the plurality of resting state networks and the nuisance node configured to receive a probability of classification as a non-active tissue selected from grey matter and CSF.
 13. The method of claim 12, wherein the plurality of resting state networks comprise a dorsal attention network (DAN), a ventral attention network (VAN), a sensorimotor network (SMN), a visual network (VIS), fronto-parietal control network (FPC), a language network (LAN), and a default mode network (DMN).
 14. The method of claim 13, further comprising training, using the computing device, the perceptron using a training data set, the training data set comprising a plurality of training correlation maps, each correlation training map further comprising a pre-defined membership in one of the plurality of resting state networks.
 15. The method of claim 14, wherein training the perceptron further comprises, for each training correlation map: transforming, using the computing device, each training correlation map to an intermediate classification of brain activity using the perceptron; comparing, using the computing device, the intermediate classification of brain activity with the pre-defined membership in at least one of the plurality of resting state networks to calculate a classification error; and updating, using the computing device, at least one of the first pre-determined weighting matrix and the second pre-determined weighting matrix to reduce the classification error.
 16. The method of claim 15, wherein comparing, using the computing device, the intermediate classification of brain activity with the pre-defined membership further comprises: calculating, using the computing device, an error e_(o) according to the equation: e _(o)(k)=d _(o)(k)−y _(o)(k), wherein: e_(o)(k) is the classification error obtained at a k^(th) iteration of training, d_(o) (k) is the predefined membership in at least one of the plurality of resting state networks, y_(o)(k) is the intermediate classification of brain activity obtained by the perceptron at the k^(th) iteration of training, and k is a training iteration index; and calculating, using the computing device, a local gradient δ_(o) of the error e₀ at the plurality of output nodes according to the equation: δ_(o) =e _(o)·φ′_(o)(v _(o)), wherein: φ′_(o)(v _(o)) is a first derivative of the pre-determined output node layer activation function.
 17. The method of claim 16, wherein updating at least one of the first pre-determined weighting matrix and the second pre-determined weighting matrix further comprises: updating, using the computing device, the second pre-determined weighting matrix according to the equation: w _(ho)(k+1)=w _(ho)(k)+η(k)·δ_(o)(k)·y _(h)(k), wherein: w_(ho)(k+1) is the updated second pre-determined weighting matrix, w_(ho)(k) is an intermediate second pre-determined weighting matrix, η(k) is a learning rate, and y_(h)(k) is a vector of intermediate hidden node values of the hidden node layer; and updating, using the computing device, the second pre-determined weighting matrix according to the equation: w _(ih)(k+1)=w _(ih)(k)+η(k)·δ_(o)(k)·y _(i)(k): wherein; w_(th)(k+1) is an updated first pre-determined weighting matrix, w_(ih)(k) is an intermediate first pre-determined weighting matrix,and y_(i)(k) is a vector of intermediate input node values of the input node layer.
 18. The method of claim 17, wherein the learning rate is an adaptive learning rate configured to increase according to the equation: ${{\eta(k)} = {A + \frac{K - A}{1 + e^{{- B} \cdot {({{\log{(k)}} - M})}}}}},$ wherein: A is an initial learning rate, B is an exponential scaling factor, and K is a total number of training iterations. 